Abstract. Relative positioning systems play a vital role in current multirobot systems. We present a self-contained detection and tracking approach, where a robot estimates a distance (range) and an angle (bearing) to another robot using measurements extracted from the raw data provided by two laser range finders. We propose a method based on the detection of circular features with least-squares fitting and filtering out outliers using a map-based selection. We improve the estimate of the relative robot position and reduce its uncertainty by feeding measurements into a Kalman filter, resulting in an accurate tracking system. We evaluate the performance of the algorithm in a realistic indoor environment to demonstrate its robustness and reliability.

Abstract-Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in realworld environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity.

This paper presents a strategy for providing reliable state estimates that allow a group of robots to realize a formation even when communication fails and the tracking data alone is insufficient for maintaining a stable formation. Furthermore, the tracking information does not provide the identity of the robot, therefore a simple fusion of tracking and communications data is not possible. We extend a Gaussian Mixture Probability Hypothesis Density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Our method of combining communicated data, information about the formation and sensory detections is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication is sporadic or suffers from shortterm outage. The proposed method is validated using a highfidelity simulator in scenarios with a formation of up to five robots. The results show that the proposed tracking strategy allows for sustaining formations in cluttered environments,

Abstract-We propose a multi-robot tracking method to provide state estimates that allow a group of robots to maintain a formation even when the communication fails. We extend a Gaussian Mixture Probability Hypothesis Density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Sensory detections, information about the formation, and communicated data are all combined in the extended Gaussian Mixture Probability Hypothesis Density filter. Our method is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication rate is slow or sporadic. The method is evaluated using a high-fidelity simulator in scenarios with a formation of up to five robots. Experiments confirm the ability of the filter to deal with occlusions and refinement of the state estimate even when poses are exchanged at a low frequency, resulting in drastic reduction of the chance of collisions compared to a tracking-free implementation.